Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.03.324665v1?rss=1 Authors: Chen, C., Zhan, L., Pan, X., Wang, Z., Guo, X., Qin, H., Xiong, F., Shi, W., Shi, M., Ji, F., Wang, Q., Yu, N., Xiao, R. Abstract: Background: Auditory brainstem response (ABR) test is widely used in newborn hearing screening and hearing disease diagnosis. Identifying and marking are challenging and repetitive tasks because of complex rules of ABR characteristic waveform and interference of background noise. Methods: This study proposes an automatic method to recognize ABR characteristic waveform. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding. Results: Specific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data, and recognition accuracy of waves I, III, and V can reach 92.91%. Discussion: Thus, the proposed method can reduce the repetitive work of doctors and meet accuracy effectively. Therefore, this method has clinical potential. Copy rights belong to original authors. Visit the link for more info